from sklearn_benchmarks.report import Reporting, ReportingHpo
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 35.0 | 9.347988 |
| daal4py_KNeighborsClassifier | 0.0 | 2.0 | 29.214814 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 42.677676 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 28.640284 |
| KMeans_tall | 0.0 | 0.0 | 23.243623 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 8.875039 |
| KMeans_short | 0.0 | 0.0 | 2.758651 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.400986 |
| LogisticRegression | 0.0 | 0.0 | 20.219920 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 4.352850 |
| Ridge | 0.0 | 0.0 | 11.123086 |
| daal4py_Ridge | 0.0 | 0.0 | 2.058851 |
| HistGradientBoostingClassifier | 0.0 | 6.0 | 17.118189 |
| lightgbm | 0.0 | 5.0 | 22.952890 |
| xgboost | 0.0 | 5.0 | 6.414030 |
| catboost | 0.0 | 5.0 | 0.956584 |
| total | 1.0 | 3.0 | 51.442933 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.231 | 0.000 | 3.465 | 0.000 | 1 | 5 | NaN | NaN | 0.508 | 0.000 | 0.455 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.161 | 0.228 | 0.000 | 0.024 | 1 | 5 | 0.831 | 0.704 | 1.754 | 0.039 | 13.776 | 0.335 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.225 | 0.002 | 0.000 | 0.225 | 1 | 5 | 1.000 | 1.000 | 0.095 | 0.001 | 2.384 | 0.026 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.145 | 0.000 | 5.536 | 0.000 | -1 | 5 | NaN | NaN | 0.496 | 0.000 | 0.291 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 36.224 | 0.000 | 0.000 | 0.036 | -1 | 5 | 0.831 | 0.942 | 1.805 | 0.013 | 20.073 | 0.147 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.200 | 0.012 | 0.000 | 0.200 | -1 | 5 | 1.000 | 1.000 | 0.095 | 0.001 | 2.107 | 0.125 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.146 | 0.000 | 5.496 | 0.000 | -1 | 1 | NaN | NaN | 0.499 | 0.000 | 0.292 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 26.064 | 0.171 | 0.000 | 0.026 | -1 | 1 | 0.714 | 0.819 | 1.772 | 0.021 | 14.707 | 0.200 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.183 | 0.013 | 0.000 | 0.183 | -1 | 1 | 1.000 | 1.000 | 0.094 | 0.001 | 1.935 | 0.144 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.150 | 0.000 | 5.345 | 0.000 | 1 | 100 | NaN | NaN | 0.497 | 0.000 | 0.301 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.273 | 0.067 | 0.000 | 0.024 | 1 | 100 | 0.939 | 0.704 | 1.764 | 0.044 | 13.762 | 0.344 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.227 | 0.002 | 0.000 | 0.227 | 1 | 100 | 1.000 | 1.000 | 0.094 | 0.000 | 2.406 | 0.027 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.140 | 0.000 | 5.728 | 0.000 | -1 | 100 | NaN | NaN | 0.496 | 0.000 | 0.282 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 36.390 | 0.000 | 0.000 | 0.036 | -1 | 100 | 0.939 | 0.942 | 1.809 | 0.012 | 20.113 | 0.129 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.198 | 0.014 | 0.000 | 0.198 | -1 | 100 | 1.000 | 1.000 | 0.095 | 0.001 | 2.078 | 0.144 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.140 | 0.000 | 5.707 | 0.000 | 1 | 1 | NaN | NaN | 0.498 | 0.000 | 0.282 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.829 | 0.028 | 0.000 | 0.014 | 1 | 1 | 0.714 | 0.819 | 1.750 | 0.014 | 7.903 | 0.063 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.211 | 0.001 | 0.000 | 0.211 | 1 | 1 | 1.000 | 1.000 | 0.098 | 0.009 | 2.154 | 0.191 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.283 | 0.000 | 1 | 5 | NaN | NaN | 0.104 | 0.000 | 0.546 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.180 | 0.037 | 0.000 | 0.020 | 1 | 5 | 0.983 | 0.986 | 0.273 | 0.014 | 74.000 | 3.828 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.001 | 0.000 | 0.020 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 3.630 | 0.297 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.288 | 0.000 | -1 | 5 | NaN | NaN | 0.102 | 0.000 | 0.543 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.290 | 0.000 | 0.000 | 0.032 | -1 | 5 | 0.983 | 0.993 | 0.310 | 0.003 | 104.136 | 1.101 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.025 | 0.003 | 0.000 | 0.025 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 4.720 | 0.698 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.282 | 0.000 | -1 | 1 | NaN | NaN | 0.102 | 0.000 | 0.555 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.392 | 0.066 | 0.000 | 0.023 | -1 | 1 | 0.974 | 0.989 | 0.262 | 0.002 | 89.330 | 0.775 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.002 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.910 | 0.529 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.055 | 0.000 | 0.289 | 0.000 | 1 | 100 | NaN | NaN | 0.102 | 0.000 | 0.542 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.212 | 0.021 | 0.000 | 0.020 | 1 | 100 | 0.978 | 0.986 | 0.265 | 0.002 | 76.205 | 0.511 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.019 | 0.001 | 0.000 | 0.019 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 3.651 | 0.353 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.055 | 0.000 | 0.291 | 0.000 | -1 | 100 | NaN | NaN | 0.102 | 0.000 | 0.537 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.236 | 0.000 | 0.000 | 0.032 | -1 | 100 | 0.978 | 0.993 | 0.311 | 0.004 | 103.666 | 1.346 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.025 | 0.002 | 0.000 | 0.025 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.592 | 0.476 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.055 | 0.000 | 0.290 | 0.000 | 1 | 1 | NaN | NaN | 0.102 | 0.000 | 0.538 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.992 | 0.028 | 0.000 | 0.011 | 1 | 1 | 0.974 | 0.989 | 0.262 | 0.002 | 41.943 | 0.273 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 2.837 | 0.282 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.970 | 0.000 | 0.027 | 0.000 | -1 | 1 | NaN | NaN | 0.715 | 0.000 | 4.152 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.457 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.974 | 0.974 | 0.195 | 0.002 | 2.347 | 0.031 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 0.000 | 0.000 | 0.000 | 6.946 | 3.205 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.986 | 0.000 | 0.027 | 0.000 | -1 | 100 | NaN | NaN | 0.699 | 0.000 | 4.269 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.800 | 0.031 | 0.000 | 0.003 | -1 | 100 | 0.976 | 0.972 | 0.579 | 0.010 | 4.834 | 0.099 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.009 | 0.001 | 0.000 | 0.009 | -1 | 100 | 1.000 | 0.000 | 0.001 | 0.000 | 7.571 | 3.064 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.961 | 0.000 | 0.027 | 0.000 | 1 | 5 | NaN | NaN | 0.700 | 0.000 | 4.227 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.482 | 0.006 | 0.000 | 0.001 | 1 | 5 | 0.977 | 0.972 | 0.579 | 0.005 | 2.561 | 0.025 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 5 | 1.000 | 0.000 | 0.001 | 0.000 | 1.995 | 0.876 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.953 | 0.000 | 0.027 | 0.000 | 1 | 100 | NaN | NaN | 0.694 | 0.000 | 4.255 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.999 | 0.061 | 0.000 | 0.005 | 1 | 100 | 0.976 | 0.959 | 0.106 | 0.003 | 47.322 | 1.325 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.007 | 0.002 | 0.000 | 0.007 | 1 | 100 | 1.000 | 0.000 | 0.000 | 0.000 | 21.958 | 10.949 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.917 | 0.000 | 0.027 | 0.000 | -1 | 5 | NaN | NaN | 0.692 | 0.000 | 4.218 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.854 | 0.007 | 0.000 | 0.001 | -1 | 5 | 0.977 | 0.974 | 0.195 | 0.002 | 4.382 | 0.067 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 0.000 | 0.000 | 0.000 | 9.092 | 4.020 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.985 | 0.000 | 0.027 | 0.000 | 1 | 1 | NaN | NaN | 0.681 | 0.000 | 4.383 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.782 | 0.004 | 0.000 | 0.001 | 1 | 1 | 0.974 | 0.959 | 0.106 | 0.002 | 7.379 | 0.146 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 1.000 | 0.000 | 0.000 | 0.000 | 4.768 | 2.407 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.763 | 0.000 | 0.021 | 0.000 | -1 | 1 | NaN | NaN | 0.439 | 0.000 | 1.735 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.002 | 0.001 | 0.000 | -1 | 1 | 0.972 | 0.982 | 0.001 | 0.000 | 22.681 | 6.653 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.998 | 16.107 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.778 | 0.000 | 0.021 | 0.000 | -1 | 100 | NaN | NaN | 0.445 | 0.000 | 1.748 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.045 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.983 | 0.985 | 0.006 | 0.001 | 7.284 | 0.815 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 18.740 | 14.866 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.763 | 0.000 | 0.021 | 0.000 | 1 | 5 | NaN | NaN | 0.434 | 0.000 | 1.758 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.003 | 0.001 | 0.000 | 1 | 5 | 0.979 | 0.985 | 0.007 | 0.001 | 3.815 | 0.704 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.025 | 3.780 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.762 | 0.000 | 0.021 | 0.000 | 1 | 100 | NaN | NaN | 0.486 | 0.000 | 1.569 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.053 | 0.001 | 0.000 | 0.000 | 1 | 100 | 0.983 | 0.972 | 0.001 | 0.000 | 70.170 | 25.399 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.870 | 5.090 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.762 | 0.000 | 0.021 | 0.000 | -1 | 5 | NaN | NaN | 0.454 | 0.000 | 1.678 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.027 | 0.000 | 0.001 | 0.000 | -1 | 5 | 0.979 | 0.982 | 0.001 | 0.000 | 24.346 | 7.568 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 18.030 | 16.735 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.817 | 0.000 | 0.020 | 0.000 | 1 | 1 | NaN | NaN | 0.456 | 0.000 | 1.792 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.972 | 0.972 | 0.001 | 0.000 | 31.104 | 12.430 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.617 | 4.798 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.627 | 0.000 | 0.765 | 0.000 | k-means++ | NaN | 30 | NaN | 0.400 | 0.0 | 1.569 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.381 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.577 | 5.591 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.631 | 8.029 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.494 | 0.000 | 0.971 | 0.000 | random | NaN | 30 | NaN | 0.391 | 0.0 | 1.265 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.379 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.214 | 4.908 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.932 | 7.218 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.683 | 0.000 | 3.591 | 0.000 | k-means++ | NaN | 30 | NaN | 3.014 | 0.0 | 2.217 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.001 | 11.465 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 7.328 | 5.073 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.019 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.626 | 6.220 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.170 | 0.000 | 3.890 | 0.000 | random | NaN | 30 | NaN | 3.350 | 0.0 | 1.842 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 15.291 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.313 | 3.063 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.772 | 7.205 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.229 | 0.0 | 0.014 | 0.000 | k-means++ | NaN | 20 | NaN | 0.088 | 0.0 | 2.609 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.192 | 0.000 | k-means++ | -0.002 | 20 | 0.002 | 0.001 | 0.0 | 2.620 | 0.449 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.446 | 7.353 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.073 | 0.0 | 0.044 | 0.000 | random | NaN | 20 | NaN | 0.029 | 0.0 | 2.499 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.191 | 0.000 | random | 0.000 | 20 | 0.000 | 0.001 | 0.0 | 2.621 | 0.490 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.815 | 6.999 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.574 | 0.0 | 0.279 | 0.000 | k-means++ | NaN | 20 | NaN | 0.327 | 0.0 | 1.756 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.697 | 0.000 | k-means++ | 0.293 | 20 | 0.298 | 0.001 | 0.0 | 2.037 | 0.382 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.560 | 4.193 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.198 | 0.0 | 0.810 | 0.000 | random | NaN | 20 | NaN | 0.131 | 0.0 | 1.508 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.765 | 0.000 | random | 0.296 | 20 | 0.302 | 0.001 | 0.0 | 2.148 | 0.321 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.676 | 4.188 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.347 | 0.0 | [-0.10398369] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.085 | 0.0 | 5.442 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [52.5790345] | 0.000 | NaN | NaN | NaN | NaN | 0.536 | 0.000 | 0.0 | 0.817 | 0.467 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.24242094] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.384 | 0.413 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.800 | 0.0 | [2.59987395] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.820 | 0.0 | 0.975 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [128.89072613] | 0.000 | NaN | NaN | NaN | NaN | 0.300 | 0.003 | 0.0 | 0.529 | 0.110 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [23.44689673] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.122 | 0.089 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.189 | 0.000 | 0.424 | 0.0 | NaN | NaN | NaN | 0.190 | 0.000 | 0.996 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.001 | 6.599 | 0.0 | NaN | NaN | 0.138 | 0.020 | 0.001 | 0.601 | 0.032 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 1.212 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.655 | 0.716 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.439 | 0.000 | 0.556 | 0.0 | NaN | NaN | NaN | 0.252 | 0.000 | 5.706 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 4.651 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.732 | 0.544 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.674 | 0.804 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()